Mapping

 In Oracle Data Integrator (ODI), a Mapping is a crucial element that defines how data flows from sources to targets, and includes all the transformations applied to the data along the way. Mappings in ODI are the core mechanism that connects data sources and destinations, and they encapsulate the rules and logic for moving and transforming data.

Key Concepts of Mappings in ODI:

1. Definition of Mappings:
• A Mapping is the logical and physical organization of the data flow between source datastores and target datastores.
• Mappings specify how data will be extracted, transformed, and loaded (ETL process), including any business rules, data transformations, and filters.
• Mappings provide a comprehensive view of the data movement process from source to target, which includes all the steps from extraction through transformation to loading.
2. Mapping Editor in ODI 12c:
• In ODI 12c, the Mapping Editor is the primary tool used to create and manage mappings.
• The Mapping Editor provides a graphical interface that allows users to design mappings visually, making it easier to define, modify, and debug data transformations.
• The Mapping Editor is launched whenever you open an existing mapping or create a new one within ODI Studio.
3. Structure and Organization of Mappings:
• Mappings are organized in folders under individual projects. These projects are found in the Projects section of the Designer Navigator.
• Projects are used to group related mappings and other components, providing an organized structure within ODI.
• A mapping folder can hold multiple mappings, each representing a specific ETL process or part of the data flow.
• Mappings can be saved, exported, and versioned within these project folders for easier management and reuse.

Components of a Mapping:

Mappings in ODI contain several key components that define the entire data flow process:

1. Source Datastore(s):
• A source datastore represents the data or data structures from which data will be extracted.
• It could be a table, file, or another type of data structure defined in the model.
• In a mapping, the source is where the data originates, and it is connected to the transformation logic that defines how data will be processed.
2. Target Datastore(s):
• A target datastore represents the destination data structure where the transformed data will be loaded.
• The target could also be a table, file, or another data structure.
• Mappings define how the extracted data will be loaded into the target system, including any necessary transformations before insertion.
3. Transformations:
• Transformations are operations or logic applied to the data as it moves from the source to the target.
• These can include a variety of operations, such as:
• Data cleansing (e.g., handling missing values)
• Aggregation (e.g., summing data or grouping records)
• Joining data from multiple sources
• Filtering to exclude unwanted records
• Calculations (e.g., performing mathematical or date functions)
• Transformations can be applied using the transformation tools provided within the Mapping Editor. These tools allow users to create custom transformation logic using a visual interface or by writing SQL expressions.
4. Flow of Data:
• Data flows between components in a mapping, starting from the source, passing through any transformations, and finally being loaded into the target.
• The data flow is defined by arrows that connect the components (sources, transformations, targets) and determine how data moves from one step to the next.
5. Business Rules and Constraints:
• Mappings can include business rules that apply specific logic to how data should be transformed or filtered.
• Constraints can be defined within the mapping to ensure that the data meets specific validation or consistency checks before being loaded into the target.
• For example, constraints might include:
• Checking for missing or invalid data before proceeding with transformations
• Enforcing referential integrity between source and target datastores
• Applying business-specific logic to data transformations (e.g., ensuring that age is calculated correctly or that data meets certain thresholds)

Types of Mappings in ODI:

ODI allows for several types of mappings, each designed to handle different types of data processing:

1. Standard Mappings:
• Standard Mappings are the most common type and represent typical ETL processes where data flows from one or more source systems into a target system.
• These mappings allow for a combination of filtering, transformations, and aggregation to prepare the data before loading into the target.
2. Incremental Update Mappings:
• Incremental Update Mappings are used when you want to load only the data that has changed since the last ETL process.
• These mappings are ideal for scenarios where you need to avoid reloading the entire dataset and instead only extract and load new or modified records.
3. Slowly Changing Dimension (SCD) Mappings:
• SCD Mappings are used to handle slowly changing dimensions in data warehouses, where attributes of a dimension change over time.
• The mapping specifies how to track historical changes to dimension data and how to load both the current and historical versions of the data.
4. Cyclic Mappings:
• Cyclic Mappings are typically used when data needs to be loaded into multiple targets from a single source with repeated cycles or updates.
• These mappings define how the data should be loaded into the target over multiple cycles, ensuring that any changes are reflected across the multiple target systems.

Mapping Execution Flow:

Once a mapping is created, it can be executed as part of an ODI scenario, which represents a deployable version of the mapping. The execution flow of a mapping typically follows these steps:

1. Execution Context:
• Before executing the mapping, ensure that the correct execution context is selected. The context defines the environment (e.g., development, production) and the specific configurations that will be used during execution.
2. Source Data Extraction:
• The data is extracted from the source datastore according to the rules defined in the mapping.
3. Transformation Logic:
• Any transformations or business rules are applied to the extracted data to process it in the required format.
4. Target Data Loading:
• The transformed data is loaded into the target datastore, either by appending new data or updating existing records based on the defined mapping rules (such as incremental updates).
5. Error Handling:
• If any errors occur during execution (e.g., data violations or transformation failures), the error handling mechanisms in ODI (such as the Check Knowledge Module (CKM)) can capture and log those errors for further resolution.
6. Completion:
• Once the mapping execution is complete, the data will have been successfully loaded into the target, and any logs or error tables can be reviewed for verification.

Benefits of Using Mappings in ODI:

1. Visual Data Integration:
• The Mapping Editor provides a graphical interface for designing and understanding complex data flows.
• This makes it easier to design, visualize, and troubleshoot the ETL process.
2. Data Transformation Flexibility:
• ODI mappings allow for a wide variety of transformations, including custom SQL logic, aggregation, and complex business rule enforcement.
3. Reuse and Maintainability:
• Mappings can be reused across multiple projects and easily modified or extended as data integration requirements change.
• The scenario feature in ODI enables deployment and management of different versions of mappings.
4. Automation of Data Flow:
• Mappings automate the ETL process, saving time and reducing errors when moving and transforming large volumes of data.

Conclusion:

Mappings in Oracle Data Integrator (ODI) are a foundational concept that helps users define, organize, and manage the movement of data between sources and targets. Using the Mapping Editor in ODI 12c, users can visually design data flows, apply transformations, and handle complex ETL logic. Mappings provide the flexibility to handle a wide range of data integration scenarios, making them an essential tool for managing data workflows and ensuring data quality and integrity.